






import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


_UpperCAmelCase      :		Tuple 					=     datasets.logging.get_logger(__name__)


_UpperCAmelCase      :		Optional[int] 					=     '\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n'

_UpperCAmelCase      :		str 					=     '\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

_UpperCAmelCase      :		int 					=     '\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

_UpperCAmelCase      :		str 					=     {
    'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
    'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
    'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
    'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
    'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
    'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
    'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
    'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
    'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
    'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,     _KWARGS_DESCRIPTION       )
class 			lowerCAmelCase_							(					datasets.Metric       ):




							def     __snake_case       ( self  : Dict						):
											return datasets.MetricInfo(
											    description=_DESCRIPTION			,				citation=_CITATION			,				homepage='''https://github.com/google-research/bleurt'''			,				inputs_description=_KWARGS_DESCRIPTION			,				features=datasets.Features(
											        {
											            '''predictions''': datasets.Value('''string'''			,				id='''sequence'''						),
											            '''references''': datasets.Value('''string'''			,				id='''sequence'''						),
											        }						)			,				codebase_urls=['''https://github.com/google-research/bleurt''']			,				reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696''']			,				)




							def     __snake_case       ( self  : Tuple			,				SCREAMING_SNAKE_CASE_  : Optional[int]						):
											# check that config name specifies a valid BLEURT model
											if self.config_name == "default":
															logger.warning(
															    '''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
															    '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'''						)
															lowerCAmelCase__         =    '''bleurt-base-128'''

											if self.config_name.lower() in CHECKPOINT_URLS:
															lowerCAmelCase__         =    self.config_name.lower()

											elif self.config_name.upper() in CHECKPOINT_URLS:
															lowerCAmelCase__         =    self.config_name.upper()

											else:
															raise KeyError(
															    f'{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'						)

											# download the model checkpoint specified by self.config_name and set up the scorer
											lowerCAmelCase__         =    dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]						)
											lowerCAmelCase__         =    score.BleurtScorer(os.path.join(lowerCAmelCase__			,				lowerCAmelCase__						)						)




							def     __snake_case       ( self  : List[str]			,				SCREAMING_SNAKE_CASE_  : Dict			,				SCREAMING_SNAKE_CASE_  : Any						):
											lowerCAmelCase__         =    self.scorer.score(references=lowerCAmelCase__			,				candidates=lowerCAmelCase__						)
											return {"scores": scores}










'''simple docstring'''



from __future__ import annotations
def        UpperCAmelCase (    A	:					list[int]       ,		A	:					int					):
							if len(A					) < k or k < 0:
														raise ValueError('''Invalid Input'''					)
							SCREAMING_SNAKE_CASE       :   Dict         =      sum(array[:k]					)
							for i in range(len(A					) - k					):
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      current_sum - array[i] + array[i + k]
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      max(A       ,		A					)
							return max_sum


if __name__ == "__main__":
							from doctest import testmod
							from random import randint

							testmod()
							lowerCAmelCase_  :							Optional[int]             =    [randint(-1000, 1000) for i in range(100)]
							lowerCAmelCase_  :							Optional[int]             =    randint(0, 110)
							print(f'The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}')




